PSDF: Prior-Driven Neural Implicit Surface Learning for Multi-view Reconstruction

📄 arXiv: 2401.12751v1 📥 PDF

作者: Wanjuan Su, Chen Zhang, Qingshan Xu, Wenbing Tao

分类: cs.CV

发布日期: 2024-01-23

💡 一句话要点

提出PSDF以解决多视图重建中的几何不一致问题

🎯 匹配领域: 支柱七：动作重定向 (Motion Retargeting)

关键词: 多视图重建 神经隐式表面 几何一致性 深度学习 计算机视觉

📋 核心要点

  1. 现有的多视图立体重建方法在处理低纹理和光照变化大的区域时，常常面临几何不一致和重建精度不足的问题。
  2. PSDF框架通过引入外部几何先验和内在几何先验，结合可见性感知损失和深度先验辅助采样，提升了神经隐式表面重建的质量。
  3. 在Tanks and Temples数据集上的实验结果显示，PSDF在复杂非受控场景中达到了最先进的性能，显著改善了重建效果。

📝 摘要（中文）

表面重建传统上依赖于基于多视图立体（MVS）的管道，但常常受到噪声和不完整几何的影响。尽管MVS在恢复场景几何方面有效，尤其是在局部细节丰富的区域，但在低纹理和光照变化大的区域，光度一致性不可靠。最近，神经隐式表面重建（NISR）结合了表面渲染和体积渲染技术，作为一种有前景的替代方案，克服了传统管道的局限性。为此，提出了PSDF框架，利用预训练MVS网络的外部几何先验和NISR模型内在的几何先验，促进高质量的神经隐式表面学习。引入了基于外部几何先验的可见性感知特征一致性损失和深度先验辅助采样，显著提高了NISR的准确性和精细重建。实验表明，PSDF在复杂的非受控场景中实现了最先进的性能。

🔬 方法详解

问题定义：本论文旨在解决传统多视图重建方法在低纹理和光照变化大的场景中几何不一致和重建精度不足的问题。现有的神经隐式表面重建方法在复杂的真实场景中面临优化不足的挑战。

核心思路：PSDF框架通过结合外部几何先验和内在几何先验，利用可见性感知特征一致性损失和深度先验辅助采样，增强了神经隐式表面学习的几何一致性和重建精度。

技术框架：整体架构包括外部几何先验的获取、内在几何先验的利用、损失函数的设计以及重建过程的优化，主要模块包括特征提取、损失计算和表面重建。

关键创新：最重要的技术创新在于引入了可见性感知特征一致性损失和深度先验辅助采样，这些方法有效地增强了几何一致性约束，帮助定位表面交点，显著提升了重建精度。

关键设计：在损失函数中，设计了可见性感知特征一致性损失以确保特征的一致性，同时利用深度先验辅助采样来优化重建过程，确保了重建表面网格的高保真度。整体网络结构经过精心设计，以适应复杂场景的需求。

🖼️ 关键图片

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📊 实验亮点

在Tanks and Temples数据集上的实验结果表明，PSDF在复杂非受控场景中达到了最先进的性能，相较于基线方法，重建精度提升了显著的百分比，具体数值未知，展示了其在几何一致性和细节恢复方面的优势。

🎯 应用场景

该研究在计算机视觉和机器人领域具有广泛的应用潜力，尤其是在自动驾驶、虚拟现实和增强现实等场景中，能够提供高质量的三维重建结果，帮助实现更为真实的环境感知与交互。未来，该技术可能推动更多基于视觉的智能系统的发展。

📄 摘要（原文）

Surface reconstruction has traditionally relied on the Multi-View Stereo (MVS)-based pipeline, which often suffers from noisy and incomplete geometry. This is due to that although MVS has been proven to be an effective way to recover the geometry of the scenes, especially for locally detailed areas with rich textures, it struggles to deal with areas with low texture and large variations of illumination where the photometric consistency is unreliable. Recently, Neural Implicit Surface Reconstruction (NISR) combines surface rendering and volume rendering techniques and bypasses the MVS as an intermediate step, which has emerged as a promising alternative to overcome the limitations of traditional pipelines. While NISR has shown impressive results on simple scenes, it remains challenging to recover delicate geometry from uncontrolled real-world scenes which is caused by its underconstrained optimization. To this end, the framework PSDF is proposed which resorts to external geometric priors from a pretrained MVS network and internal geometric priors inherent in the NISR model to facilitate high-quality neural implicit surface learning. Specifically, the visibility-aware feature consistency loss and depth prior-assisted sampling based on external geometric priors are introduced. These proposals provide powerfully geometric consistency constraints and aid in locating surface intersection points, thereby significantly improving the accuracy and delicate reconstruction of NISR. Meanwhile, the internal prior-guided importance rendering is presented to enhance the fidelity of the reconstructed surface mesh by mitigating the biased rendering issue in NISR. Extensive experiments on the Tanks and Temples dataset show that PSDF achieves state-of-the-art performance on complex uncontrolled scenes.